Assignment -
You may use: Pandas, NumPy, Json, seaborn, decision tree,
Bokeh
- from bokeh.plotting import figure
- from bokeh.io import show, output_file
- from bokeh.core.properties import value
- from bokeh.models import ColumnDataSource, Label
sklearn
- from sklearn.model_selection import train_test_split
- from sklearn.preprocessing import StandardScaler
- from sklearn.neural_network import MLPClassifier
- from sklearn.metrics import classification_report, confusion_matrix
Using csv file.
1) I want to test if pollution increases the prevalence of cancer types
2) Higher pollution levels lead to higher prevalence of lung cancer and leukemia
3) Higher pollution levels do NOT lead to a higher prevalence of lung cancer and leukemia
4) Build out a data set of cancer prevalence by state, then correlate occurrences vs pollution levels using graphs and the two data sets
Be sure to follow these general guidelines:
- The code must produce at least one graph
- The code must perform at least one additional analysis (summary stat, machine learning, etc)
- The code should include functions for better organized code
- The code should be commented
Brief Summary -
- Data set - (For example) this analysis will focus on which columns?
- Hypotheses
- Test plan
- Present the result - Include figures!! Maybe screenshots of console output as well
- Analyze results - Were you right? Were you wrong? And What would you do different next time?
Attachment:- Assignment File.rar